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动静分离网络

news 2025/6/26 1:19:22

动静分离网络的主要目的是分别处理视频帧中的静止区域和运动区域，以便对不同区域采用不同的去噪策略。这里提供一个实现思路，通过两个分支网络分别处理静止区域和运动区域，然后将两者的输出融合起来。

实现步骤

帧差图生成：计算帧差图来识别运动区域和静止区域。
动静分离网络设计：构建两个分支网络，一个处理静止区域，另一个处理运动区域。
融合输出：将两个分支的输出融合，得到最终的去噪结果。

具体实现

1. 帧差图生成

首先，计算当前帧和前一帧的差异，得到帧差图：

import torchdef compute_frame_diff(current_frame, previous_frame):return torch.abs(current_frame - previous_frame)

2. 动静分离网络设计

构建两个分支网络，分别处理静止区域和运动区域：

import torch.nn as nn
import torch.nn.functional as Fclass StaticBranch(nn.Module):def __init__(self):super(StaticBranch, self).__init__()self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)self.conv3 = nn.Conv2d(64, 3, kernel_size=3, padding=1)def forward(self, x):x = F.relu(self.conv1(x))x = F.relu(self.conv2(x))x = self.conv3(x)return xclass MotionBranch(nn.Module):def __init__(self):super(MotionBranch, self).__init__()self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)self.conv3 = nn.Conv2d(64, 3, kernel_size=3, padding=1)def forward(self, x):x = F.relu(self.conv1(x))x = F.relu(self.conv2(x))x = self.conv3(x)return xclass MotionStaticDenoiseNet(nn.Module):def __init__(self):super(MotionStaticDenoiseNet, self).__init__()self.static_branch = StaticBranch()self.motion_branch = MotionBranch()def forward(self, x, motion_map):static_output = self.static_branch(x)motion_output = self.motion_branch(x)# 根据motion_map进行融合output = motion_output * motion_map + static_output * (1 - motion_map)return output

综合应用

利用动静分离网络进行去噪：

def denoise_image(current_frame, previous_frame, model):motion_map = compute_frame_diff(current_frame, previous_frame)motion_map = (motion_map > 0.1).float()  # 设定一个阈值，得到二值化的动静权重图denoised_image = model(current_frame, motion_map)return denoised_image

执行示例

假设 current_frame 是当前帧，previous_frame 是前一帧，model 是动静分离网络：

model = MotionStaticDenoiseNet()
current_frame = torch.randn(1, 3, 256, 256)  # 示例当前帧
previous_frame = torch.randn(1, 3, 256, 256)  # 示例前一帧denoised_image = denoise_image(current_frame, previous_frame, model)